{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9fb9267f-7262-4c77-83ae-ee1adb0e0217",
   "metadata": {},
   "source": [
    "### 文本预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "72ee6d0d-8647-42a5-ad15-dd804564236c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4b88d8da-53e7-481b-87ff-e7dbbca86fce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#text lines: 3221\n",
      "twinkled and his usually pale face was flushed and animated the\n"
     ]
    }
   ],
   "source": [
    "#读取数据\n",
    "def read_data():\n",
    "    with open('./article.txt', 'r') as f:\n",
    "        lines = f.readlines()\n",
    "    return [re.sub('[^A-Za-z]+', ' ', line).strip().lower() for line in lines]\n",
    "lines = read_data()\n",
    "print(f'#text lines: {len(lines)}')\n",
    "print(lines[10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "54fc6738-f927-417e-9198-8de21f379516",
   "metadata": {},
   "outputs": [],
   "source": [
    "#词元化\n",
    "def tokenize(lines, token='word'):\n",
    "    if token == 'word':\n",
    "        return [line.split() for line in lines]\n",
    "    elif token == 'char':\n",
    "        return [list(line) for line in lines]\n",
    "    else: \n",
    "        print('Unknow Token Type.')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "dd3f211a-dd09-4ea3-9216-d855576b9d54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计词元频率\n",
    "def count_corpus(tokens):\n",
    "    if len(tokens) == 0 or isinstance(tokens[0], list):\n",
    "        # 把词元列表展平成使用词元填充的一个列表. \n",
    "        tokens = [token for line in tokens for token in line]\n",
    "    return collections.Counter(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8d29102a-cf26-47ba-a8b5-351dac8e9c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens = tokenize(lines, 'char')\n",
    "len(count_corpus(tokens))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "adf26dab-dd89-487b-9f34-10125dfdacc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('t', 13515), ('h', 8257), ('e', 17838), (' ', 29927), ('i', 10138), ('m', 4043), ('a', 11704), ('c', 3424), ('n', 9917), ('b', 1897), ('y', 2679), ('g', 3075), ('w', 3225), ('l', 6146), ('s', 8486), ('r', 7674), ('v', 1295), ('f', 3354), ('o', 9758), ('p', 2427), ('k', 1087), ('x', 236), ('u', 3805), ('d', 6337), ('z', 144), ('j', 97), ('q', 95)])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_corpus(tokens).items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f50958a6-98c5-4e28-b8b8-d8811f1cab26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建词汇表\n",
    "class Vocab:\n",
    "    \"\"\"文本词汇表\"\"\"\n",
    "    def __init__(self, tokens=None, min_freq=0, reserved_token=None):\n",
    "        if tokens is None:\n",
    "            tokens = []\n",
    "        if reserved_token is None:\n",
    "            reserved_token = []\n",
    "            \n",
    "        # 按照出现频率进行排序\n",
    "        counter = count_corpus(tokens)\n",
    "        self.token_freqs = sorted(counter.items(), key=lambda x: x[1], reverse=True)\n",
    "        # 未知词元索引为0\n",
    "        self.unk, uniq_tokens = 0, ['<unk>'] + reserved_token\n",
    "        uniq_tokens += [token for token, freq in self.token_freqs if freq >= min_freq and token not in uniq_tokens]\n",
    "        self.idx_to_token, self.token_to_idx = [], dict()\n",
    "        \n",
    "        for token in uniq_tokens:\n",
    "            self.idx_to_token.append(token)\n",
    "            self.token_to_idx[token] = len(self.idx_to_token) - 1\n",
    "            \n",
    "    def __len__(self):\n",
    "        return len(self.idx_to_token)\n",
    "    \n",
    "    def __getitem__(self, tokens):\n",
    "        if not isinstance(tokens, (list, tuple)):\n",
    "            return self.token_to_idx.get(tokens, self.unk)\n",
    "        return [self.__getitem__(token) for token in tokens]\n",
    "    \n",
    "    def to_tokens(self, indices):\n",
    "        if not isinstance(indices, (list, tuple)):\n",
    "            return self.idx_to_token[indices]\n",
    "        return [self.idx_to_token[index] for index in indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "410c80d3-6b9f-4b09-b279-0866679f6993",
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab = Vocab(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b304a28b-52dd-471e-bb57-ec51e758361e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ff1a3bfb-e838-4d45-9834-804ab4207441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'<unk>': 0,\n",
       " ' ': 1,\n",
       " 'e': 2,\n",
       " 't': 3,\n",
       " 'a': 4,\n",
       " 'i': 5,\n",
       " 'n': 6,\n",
       " 'o': 7,\n",
       " 's': 8,\n",
       " 'h': 9,\n",
       " 'r': 10,\n",
       " 'd': 11,\n",
       " 'l': 12,\n",
       " 'm': 13,\n",
       " 'u': 14,\n",
       " 'c': 15,\n",
       " 'f': 16,\n",
       " 'w': 17,\n",
       " 'g': 18,\n",
       " 'y': 19,\n",
       " 'p': 20,\n",
       " 'b': 21,\n",
       " 'v': 22,\n",
       " 'k': 23,\n",
       " 'x': 24,\n",
       " 'z': 25,\n",
       " 'j': 26,\n",
       " 'q': 27}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab.token_to_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ff25d22e-4d77-490c-9fba-ed676627f090",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 整合所有功能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "be8e3425-e292-4bf9-b800-32d21810b54c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_corpus_time_machine(max_tokens=-1):\n",
    "    \"\"\"返回时光机器文本数据集中的词元索引和词汇表\"\"\"\n",
    "    lines = read_data()\n",
    "    tokens = tokenize(lines, 'char')\n",
    "    vocab = Vocab(tokens)\n",
    "    # 把所有文本行展平到一个列表\n",
    "    corpus = [vocab[token] for line in tokens for token in line]\n",
    "    if max_tokens > 0:\n",
    "        corpus = corpus[:max_tokens]\n",
    "    return corpus, vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "71dd93bf-ba3f-412d-be91-d35d944b4a07",
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus, vocab = load_corpus_time_machine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a2f13756-449f-4255-9f68-f5b836aa1807",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170580"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d0276c7a-ed23-4544-b3ea-5965bf862652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc71e1f9-3068-4f39-9d79-cd85b160dd84",
   "metadata": {},
   "source": [
    "### 语言模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bfc1a025-b88f-4840-b731-bbb6eddfc2c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#我是中国人 T=5, w1, w2, w3, w4, w5 语言模型的目标就是评估该序列是否合理. 怎么评估?\n",
    "#计算该序列的概率. P(w1, w2, w3, w4, w5) = P(我)P(是|我)P(中|我, 是)P(国|我, 是, 中)P(人|我, 是, 中, 国) P(我是中国人) > P(我是中华人)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d04d6857-9bd0-4f69-9354-0cd8e666e44b",
   "metadata": {},
   "source": [
    "#### 一元语法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "806b9cee-d6d6-4f24-8d33-4dc78e274a2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['the', 'time', 'machine', 'by', 'h', 'g', 'wells'],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " ['i'],\n",
       " [],\n",
       " [],\n",
       " ['the',\n",
       "  'time',\n",
       "  'traveller',\n",
       "  'for',\n",
       "  'so',\n",
       "  'it',\n",
       "  'will',\n",
       "  'be',\n",
       "  'convenient',\n",
       "  'to',\n",
       "  'speak',\n",
       "  'of',\n",
       "  'him'],\n",
       " ['was',\n",
       "  'expounding',\n",
       "  'a',\n",
       "  'recondite',\n",
       "  'matter',\n",
       "  'to',\n",
       "  'us',\n",
       "  'his',\n",
       "  'grey',\n",
       "  'eyes',\n",
       "  'shone',\n",
       "  'and'],\n",
       " ['twinkled',\n",
       "  'and',\n",
       "  'his',\n",
       "  'usually',\n",
       "  'pale',\n",
       "  'face',\n",
       "  'was',\n",
       "  'flushed',\n",
       "  'and',\n",
       "  'animated',\n",
       "  'the'],\n",
       " ['fire',\n",
       "  'burned',\n",
       "  'brightly',\n",
       "  'and',\n",
       "  'the',\n",
       "  'soft',\n",
       "  'radiance',\n",
       "  'of',\n",
       "  'the',\n",
       "  'incandescent'],\n",
       " ['lights',\n",
       "  'in',\n",
       "  'the',\n",
       "  'lilies',\n",
       "  'of',\n",
       "  'silver',\n",
       "  'caught',\n",
       "  'the',\n",
       "  'bubbles',\n",
       "  'that',\n",
       "  'flashed',\n",
       "  'and'],\n",
       " ['passed',\n",
       "  'in',\n",
       "  'our',\n",
       "  'glasses',\n",
       "  'our',\n",
       "  'chairs',\n",
       "  'being',\n",
       "  'his',\n",
       "  'patents',\n",
       "  'embraced',\n",
       "  'and'],\n",
       " ['caressed',\n",
       "  'us',\n",
       "  'rather',\n",
       "  'than',\n",
       "  'submitted',\n",
       "  'to',\n",
       "  'be',\n",
       "  'sat',\n",
       "  'upon',\n",
       "  'and',\n",
       "  'there',\n",
       "  'was',\n",
       "  'that'],\n",
       " ['luxurious',\n",
       "  'after',\n",
       "  'dinner',\n",
       "  'atmosphere',\n",
       "  'when',\n",
       "  'thought',\n",
       "  'roams',\n",
       "  'gracefully'],\n",
       " ['free',\n",
       "  'of',\n",
       "  'the',\n",
       "  'trammels',\n",
       "  'of',\n",
       "  'precision',\n",
       "  'and',\n",
       "  'he',\n",
       "  'put',\n",
       "  'it',\n",
       "  'to',\n",
       "  'us',\n",
       "  'in',\n",
       "  'this'],\n",
       " ['way',\n",
       "  'marking',\n",
       "  'the',\n",
       "  'points',\n",
       "  'with',\n",
       "  'a',\n",
       "  'lean',\n",
       "  'forefinger',\n",
       "  'as',\n",
       "  'we',\n",
       "  'sat',\n",
       "  'and',\n",
       "  'lazily'],\n",
       " ['admired',\n",
       "  'his',\n",
       "  'earnestness',\n",
       "  'over',\n",
       "  'this',\n",
       "  'new',\n",
       "  'paradox',\n",
       "  'as',\n",
       "  'we',\n",
       "  'thought',\n",
       "  'it'],\n",
       " ['and', 'his', 'fecundity'],\n",
       " [],\n",
       " ['you',\n",
       "  'must',\n",
       "  'follow',\n",
       "  'me',\n",
       "  'carefully',\n",
       "  'i',\n",
       "  'shall',\n",
       "  'have',\n",
       "  'to',\n",
       "  'controvert',\n",
       "  'one',\n",
       "  'or',\n",
       "  'two'],\n",
       " ['ideas',\n",
       "  'that',\n",
       "  'are',\n",
       "  'almost',\n",
       "  'universally',\n",
       "  'accepted',\n",
       "  'the',\n",
       "  'geometry',\n",
       "  'for'],\n",
       " ['instance',\n",
       "  'they',\n",
       "  'taught',\n",
       "  'you',\n",
       "  'at',\n",
       "  'school',\n",
       "  'is',\n",
       "  'founded',\n",
       "  'on',\n",
       "  'a',\n",
       "  'misconception'],\n",
       " [],\n",
       " ['is',\n",
       "  'not',\n",
       "  'that',\n",
       "  'rather',\n",
       "  'a',\n",
       "  'large',\n",
       "  'thing',\n",
       "  'to',\n",
       "  'expect',\n",
       "  'us',\n",
       "  'to',\n",
       "  'begin',\n",
       "  'upon'],\n",
       " ['said', 'filby', 'an', 'argumentative', 'person', 'with', 'red', 'hair'],\n",
       " [],\n",
       " ['i',\n",
       "  'do',\n",
       "  'not',\n",
       "  'mean',\n",
       "  'to',\n",
       "  'ask',\n",
       "  'you',\n",
       "  'to',\n",
       "  'accept',\n",
       "  'anything',\n",
       "  'without',\n",
       "  'reasonable'],\n",
       " ['ground',\n",
       "  'for',\n",
       "  'it',\n",
       "  'you',\n",
       "  'will',\n",
       "  'soon',\n",
       "  'admit',\n",
       "  'as',\n",
       "  'much',\n",
       "  'as',\n",
       "  'i',\n",
       "  'need',\n",
       "  'from',\n",
       "  'you',\n",
       "  'you'],\n",
       " ['know',\n",
       "  'of',\n",
       "  'course',\n",
       "  'that',\n",
       "  'a',\n",
       "  'mathematical',\n",
       "  'line',\n",
       "  'a',\n",
       "  'line',\n",
       "  'of',\n",
       "  'thickness',\n",
       "  'nil'],\n",
       " ['has',\n",
       "  'no',\n",
       "  'real',\n",
       "  'existence',\n",
       "  'they',\n",
       "  'taught',\n",
       "  'you',\n",
       "  'that',\n",
       "  'neither',\n",
       "  'has',\n",
       "  'a'],\n",
       " ['mathematical', 'plane', 'these', 'things', 'are', 'mere', 'abstractions'],\n",
       " [],\n",
       " ['that', 'is', 'all', 'right', 'said', 'the', 'psychologist'],\n",
       " [],\n",
       " ['nor',\n",
       "  'having',\n",
       "  'only',\n",
       "  'length',\n",
       "  'breadth',\n",
       "  'and',\n",
       "  'thickness',\n",
       "  'can',\n",
       "  'a',\n",
       "  'cube',\n",
       "  'have',\n",
       "  'a'],\n",
       " ['real', 'existence'],\n",
       " [],\n",
       " ['there',\n",
       "  'i',\n",
       "  'object',\n",
       "  'said',\n",
       "  'filby',\n",
       "  'of',\n",
       "  'course',\n",
       "  'a',\n",
       "  'solid',\n",
       "  'body',\n",
       "  'may',\n",
       "  'exist',\n",
       "  'all'],\n",
       " ['real', 'things'],\n",
       " [],\n",
       " ['so',\n",
       "  'most',\n",
       "  'people',\n",
       "  'think',\n",
       "  'but',\n",
       "  'wait',\n",
       "  'a',\n",
       "  'moment',\n",
       "  'can',\n",
       "  'an',\n",
       "  'instantaneous'],\n",
       " ['cube', 'exist'],\n",
       " [],\n",
       " ['don', 't', 'follow', 'you', 'said', 'filby'],\n",
       " [],\n",
       " ['can',\n",
       "  'a',\n",
       "  'cube',\n",
       "  'that',\n",
       "  'does',\n",
       "  'not',\n",
       "  'last',\n",
       "  'for',\n",
       "  'any',\n",
       "  'time',\n",
       "  'at',\n",
       "  'all',\n",
       "  'have',\n",
       "  'a',\n",
       "  'real'],\n",
       " ['existence'],\n",
       " [],\n",
       " ['filby',\n",
       "  'became',\n",
       "  'pensive',\n",
       "  'clearly',\n",
       "  'the',\n",
       "  'time',\n",
       "  'traveller',\n",
       "  'proceeded',\n",
       "  'any'],\n",
       " ['real',\n",
       "  'body',\n",
       "  'must',\n",
       "  'have',\n",
       "  'extension',\n",
       "  'in',\n",
       "  'four',\n",
       "  'directions',\n",
       "  'it',\n",
       "  'must',\n",
       "  'have'],\n",
       " ['length',\n",
       "  'breadth',\n",
       "  'thickness',\n",
       "  'and',\n",
       "  'duration',\n",
       "  'but',\n",
       "  'through',\n",
       "  'a',\n",
       "  'natural'],\n",
       " ['infirmity',\n",
       "  'of',\n",
       "  'the',\n",
       "  'flesh',\n",
       "  'which',\n",
       "  'i',\n",
       "  'will',\n",
       "  'explain',\n",
       "  'to',\n",
       "  'you',\n",
       "  'in',\n",
       "  'a',\n",
       "  'moment',\n",
       "  'we'],\n",
       " ['incline',\n",
       "  'to',\n",
       "  'overlook',\n",
       "  'this',\n",
       "  'fact',\n",
       "  'there',\n",
       "  'are',\n",
       "  'really',\n",
       "  'four',\n",
       "  'dimensions'],\n",
       " ['three',\n",
       "  'which',\n",
       "  'we',\n",
       "  'call',\n",
       "  'the',\n",
       "  'three',\n",
       "  'planes',\n",
       "  'of',\n",
       "  'space',\n",
       "  'and',\n",
       "  'a',\n",
       "  'fourth',\n",
       "  'time'],\n",
       " ['there',\n",
       "  'is',\n",
       "  'however',\n",
       "  'a',\n",
       "  'tendency',\n",
       "  'to',\n",
       "  'draw',\n",
       "  'an',\n",
       "  'unreal',\n",
       "  'distinction',\n",
       "  'between'],\n",
       " ['the',\n",
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       "  'some',\n",
       "  'vast',\n",
       "  'structure',\n",
       "  'to',\n",
       "  'what',\n",
       "  'end',\n",
       "  'built',\n",
       "  'i',\n",
       "  'could',\n",
       "  'not'],\n",
       " ['determine',\n",
       "  'it',\n",
       "  'was',\n",
       "  'here',\n",
       "  'that',\n",
       "  'i',\n",
       "  'was',\n",
       "  'destined',\n",
       "  'at',\n",
       "  'a',\n",
       "  'later',\n",
       "  'date',\n",
       "  'to',\n",
       "  'have'],\n",
       " ['a',\n",
       "  'very',\n",
       "  'strange',\n",
       "  'experience',\n",
       "  'the',\n",
       "  'first',\n",
       "  'intimation',\n",
       "  'of',\n",
       "  'a',\n",
       "  'still',\n",
       "  'stranger'],\n",
       " ['discovery',\n",
       "  'but',\n",
       "  'of',\n",
       "  'that',\n",
       "  'i',\n",
       "  'will',\n",
       "  'speak',\n",
       "  'in',\n",
       "  'its',\n",
       "  'proper',\n",
       "  'place'],\n",
       " [],\n",
       " ['looking',\n",
       "  'round',\n",
       "  'with',\n",
       "  'a',\n",
       "  'sudden',\n",
       "  'thought',\n",
       "  'from',\n",
       "  'a',\n",
       "  'terrace',\n",
       "  'on',\n",
       "  'which',\n",
       "  'i'],\n",
       " ['rested',\n",
       "  'for',\n",
       "  'a',\n",
       "  'while',\n",
       "  'i',\n",
       "  'realized',\n",
       "  'that',\n",
       "  'there',\n",
       "  'were',\n",
       "  'no',\n",
       "  'small',\n",
       "  'houses',\n",
       "  'to',\n",
       "  'be'],\n",
       " ['seen',\n",
       "  'apparently',\n",
       "  'the',\n",
       "  'single',\n",
       "  'house',\n",
       "  'and',\n",
       "  'possibly',\n",
       "  'even',\n",
       "  'the',\n",
       "  'household'],\n",
       " ['had',\n",
       "  'vanished',\n",
       "  'here',\n",
       "  'and',\n",
       "  'there',\n",
       "  'among',\n",
       "  'the',\n",
       "  'greenery',\n",
       "  'were',\n",
       "  'palace',\n",
       "  'like'],\n",
       " ['buildings',\n",
       "  'but',\n",
       "  'the',\n",
       "  'house',\n",
       "  'and',\n",
       "  'the',\n",
       "  'cottage',\n",
       "  'which',\n",
       "  'form',\n",
       "  'such'],\n",
       " ['characteristic',\n",
       "  'features',\n",
       "  'of',\n",
       "  'our',\n",
       "  'own',\n",
       "  'english',\n",
       "  'landscape',\n",
       "  'had'],\n",
       " ['disappeared'],\n",
       " [],\n",
       " ['communism', 'said', 'i', 'to', 'myself'],\n",
       " [],\n",
       " ['and',\n",
       "  'on',\n",
       "  'the',\n",
       "  'heels',\n",
       "  'of',\n",
       "  'that',\n",
       "  'came',\n",
       "  'another',\n",
       "  'thought',\n",
       "  'i',\n",
       "  'looked',\n",
       "  'at',\n",
       "  'the'],\n",
       " ['half',\n",
       "  'dozen',\n",
       "  'little',\n",
       "  'figures',\n",
       "  'that',\n",
       "  'were',\n",
       "  'following',\n",
       "  'me',\n",
       "  'then',\n",
       "  'in',\n",
       "  'a',\n",
       "  'flash'],\n",
       " ['i',\n",
       "  'perceived',\n",
       "  'that',\n",
       "  'all',\n",
       "  'had',\n",
       "  'the',\n",
       "  'same',\n",
       "  'form',\n",
       "  'of',\n",
       "  'costume',\n",
       "  'the',\n",
       "  'same',\n",
       "  'soft'],\n",
       " ['hairless',\n",
       "  'visage',\n",
       "  'and',\n",
       "  'the',\n",
       "  'same',\n",
       "  'girlish',\n",
       "  'rotundity',\n",
       "  'of',\n",
       "  'limb',\n",
       "  'it',\n",
       "  'may',\n",
       "  'seem'],\n",
       " ...]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens = tokenize(read_data())\n",
    "tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "faba2588-62b1-4f20-85e9-89d1276279df",
   "metadata": {},
   "outputs": [],
   "source": [
    "#马尔科夫假设: 一个词的出现只与前面N个词相关, n阶马尔科夫链(Markov chain of order n)\n",
    "#你走先, 你先走 在一元语法中概率一样.\n",
    "#n元语法的缺点: 1. 参数空间过大 2. 数据会比较稀疏\n",
    "#齐夫定律: 在一个自然语言语料库中, 某个单词出现的频率与其在频率表中名词的常数次幂成反比. 说人话: 极少数的单词会被经常使用到, 绝大多数单词很少被提及."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3ed0bed8-3ca5-409a-bae1-242dd6a5d29f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "05c1789b-dee9-4756-bffe-09d7eba8f51d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 29927,\n",
       "         2: 17838,\n",
       "         3: 13515,\n",
       "         4: 11704,\n",
       "         5: 10138,\n",
       "         6: 9917,\n",
       "         7: 9758,\n",
       "         8: 8486,\n",
       "         9: 8257,\n",
       "         10: 7674,\n",
       "         11: 6337,\n",
       "         12: 6146,\n",
       "         13: 4043,\n",
       "         14: 3805,\n",
       "         15: 3424,\n",
       "         16: 3354,\n",
       "         17: 3225,\n",
       "         18: 3075,\n",
       "         19: 2679,\n",
       "         20: 2427,\n",
       "         21: 1897,\n",
       "         22: 1295,\n",
       "         23: 1087,\n",
       "         24: 236,\n",
       "         25: 144,\n",
       "         26: 97,\n",
       "         27: 95})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_corpus(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0379f0f0-4b35-4132-b7f1-df8cba9ea0ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把所有文本行拼接在一起. \n",
    "corpus = [token for line in tokens for token in line]\n",
    "#词汇表\n",
    "vocab = Vocab(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "38cd9f08-2b37-42db-b3e7-82f7ea006389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('the', 2261),\n",
       " ('i', 1267),\n",
       " ('and', 1245),\n",
       " ('of', 1155),\n",
       " ('a', 816),\n",
       " ('to', 695),\n",
       " ('was', 552),\n",
       " ('in', 541),\n",
       " ('that', 443),\n",
       " ('my', 440),\n",
       " ('it', 437),\n",
       " ('had', 354),\n",
       " ('me', 281),\n",
       " ('as', 270),\n",
       " ('at', 243),\n",
       " ('for', 221),\n",
       " ('with', 216),\n",
       " ('but', 204),\n",
       " ('time', 200),\n",
       " ('were', 158),\n",
       " ('this', 152),\n",
       " ('you', 137),\n",
       " ('on', 137),\n",
       " ('then', 134),\n",
       " ('his', 129),\n",
       " ('there', 127),\n",
       " ('he', 123),\n",
       " ('have', 122),\n",
       " ('they', 122),\n",
       " ('from', 122),\n",
       " ('one', 120),\n",
       " ('all', 118),\n",
       " ('not', 114),\n",
       " ('into', 114),\n",
       " ('upon', 113),\n",
       " ('little', 113),\n",
       " ('so', 112),\n",
       " ('is', 106),\n",
       " ('came', 105),\n",
       " ('by', 103),\n",
       " ('some', 94),\n",
       " ('be', 93),\n",
       " ('no', 92),\n",
       " ('could', 92),\n",
       " ('their', 91),\n",
       " ('said', 89),\n",
       " ('saw', 88),\n",
       " ('down', 87),\n",
       " ('them', 86),\n",
       " ('machine', 85),\n",
       " ('which', 85),\n",
       " ('very', 85),\n",
       " ('or', 84),\n",
       " ('an', 84),\n",
       " ('we', 82),\n",
       " ('now', 79),\n",
       " ('what', 77),\n",
       " ('been', 75),\n",
       " ('these', 74),\n",
       " ('like', 74),\n",
       " ('her', 74),\n",
       " ('out', 73),\n",
       " ('seemed', 72),\n",
       " ('up', 71),\n",
       " ('man', 70),\n",
       " ('about', 70),\n",
       " ('s', 70),\n",
       " ('its', 69),\n",
       " ('thing', 66),\n",
       " ('again', 62),\n",
       " ('traveller', 61),\n",
       " ('would', 60),\n",
       " ('more', 59),\n",
       " ('white', 59),\n",
       " ('our', 57),\n",
       " ('thought', 57),\n",
       " ('felt', 57),\n",
       " ('when', 55),\n",
       " ('over', 54),\n",
       " ('weena', 54),\n",
       " ('still', 53),\n",
       " ('world', 52),\n",
       " ('myself', 51),\n",
       " ('even', 50),\n",
       " ('must', 49),\n",
       " ('through', 49),\n",
       " ('if', 49),\n",
       " ('hand', 49),\n",
       " ('went', 49),\n",
       " ('first', 49),\n",
       " ('are', 48),\n",
       " ('before', 48),\n",
       " ('last', 47),\n",
       " ('towards', 47),\n",
       " ('only', 46),\n",
       " ('people', 46),\n",
       " ('she', 46),\n",
       " ('morlocks', 46),\n",
       " ('see', 45),\n",
       " ('too', 45),\n",
       " ('found', 44),\n",
       " ('how', 43),\n",
       " ('here', 43),\n",
       " ('light', 43),\n",
       " ('great', 42),\n",
       " ('under', 42),\n",
       " ('did', 41),\n",
       " ('him', 40),\n",
       " ('any', 40),\n",
       " ('began', 40),\n",
       " ('back', 40),\n",
       " ('night', 39),\n",
       " ('face', 38),\n",
       " ('way', 38),\n",
       " ('will', 37),\n",
       " ('after', 37),\n",
       " ('another', 37),\n",
       " ('well', 37),\n",
       " ('same', 37),\n",
       " ('think', 36),\n",
       " ('other', 36),\n",
       " ('away', 36),\n",
       " ('round', 36),\n",
       " ('made', 36),\n",
       " ('day', 36),\n",
       " ('us', 35),\n",
       " ('eyes', 35),\n",
       " ('mind', 35),\n",
       " ('might', 35),\n",
       " ('perhaps', 35),\n",
       " ('than', 34),\n",
       " ('put', 34),\n",
       " ('things', 34),\n",
       " ('long', 34),\n",
       " ('looked', 34),\n",
       " ('own', 34),\n",
       " ('may', 33),\n",
       " ('among', 33),\n",
       " ('sky', 33),\n",
       " ('such', 32),\n",
       " ('against', 32),\n",
       " ('took', 32),\n",
       " ('strange', 32),\n",
       " ('yet', 32),\n",
       " ('moment', 31),\n",
       " ('where', 31),\n",
       " ('sun', 31),\n",
       " ('fire', 30),\n",
       " ('black', 30),\n",
       " ('come', 30),\n",
       " ('know', 29),\n",
       " ('off', 29),\n",
       " ('new', 28),\n",
       " ('two', 28),\n",
       " ('old', 28),\n",
       " ('enough', 28),\n",
       " ('hands', 28),\n",
       " ('presently', 28),\n",
       " ('most', 27),\n",
       " ('t', 27),\n",
       " ('dark', 27),\n",
       " ('darkness', 27),\n",
       " ('once', 27),\n",
       " ('red', 26),\n",
       " ('who', 26),\n",
       " ('left', 26),\n",
       " ('green', 26),\n",
       " ('grew', 26),\n",
       " ('place', 26),\n",
       " ('hill', 26),\n",
       " ('psychologist', 25),\n",
       " ('space', 25),\n",
       " ('end', 25),\n",
       " ('got', 25),\n",
       " ('three', 24),\n",
       " ('looking', 24),\n",
       " ('medical', 24),\n",
       " ('stood', 24),\n",
       " ('fear', 24),\n",
       " ('almost', 23),\n",
       " ('much', 23),\n",
       " ('should', 23),\n",
       " ('above', 23),\n",
       " ('air', 23),\n",
       " ('head', 23),\n",
       " ('sat', 22),\n",
       " ('do', 22),\n",
       " ('has', 22),\n",
       " ('can', 22),\n",
       " ('tried', 22),\n",
       " ('far', 22),\n",
       " ('seen', 22),\n",
       " ('minute', 22),\n",
       " ('suddenly', 22),\n",
       " ('across', 22),\n",
       " ('sphinx', 22),\n",
       " ('soon', 21),\n",
       " ('along', 21),\n",
       " ('side', 21),\n",
       " ('get', 21),\n",
       " ('earth', 21),\n",
       " ('future', 21),\n",
       " ('part', 21),\n",
       " ('turned', 21),\n",
       " ('human', 21),\n",
       " ('indeed', 20),\n",
       " ('heard', 20),\n",
       " ('say', 20),\n",
       " ('kind', 20),\n",
       " ('gone', 20),\n",
       " ('never', 20),\n",
       " ('until', 20),\n",
       " ('make', 20),\n",
       " ('look', 20),\n",
       " ('while', 20),\n",
       " ('half', 20),\n",
       " ('editor', 20),\n",
       " ('feeling', 20),\n",
       " ('though', 19),\n",
       " ('go', 19),\n",
       " ('room', 19),\n",
       " ('laboratory', 19),\n",
       " ('already', 19),\n",
       " ('moon', 19),\n",
       " ('bronze', 19),\n",
       " ('flowers', 19),\n",
       " ('match', 19),\n",
       " ('matches', 19),\n",
       " ('gallery', 19),\n",
       " ('rather', 18),\n",
       " ('cannot', 18),\n",
       " ('feet', 18),\n",
       " ('certain', 18),\n",
       " ('those', 18),\n",
       " ('filby', 17),\n",
       " ('ground', 17),\n",
       " ('years', 17),\n",
       " ('just', 17),\n",
       " ('table', 17),\n",
       " ('lever', 17),\n",
       " ('big', 17),\n",
       " ('ran', 17),\n",
       " ('door', 17),\n",
       " ('soft', 16),\n",
       " ('mere', 16),\n",
       " ('don', 16),\n",
       " ('however', 16),\n",
       " ('between', 16),\n",
       " ('why', 16),\n",
       " ('fell', 16),\n",
       " ('morning', 16),\n",
       " ('something', 16),\n",
       " ('going', 16),\n",
       " ('behind', 16),\n",
       " ('past', 16),\n",
       " ('followed', 16),\n",
       " ('lay', 16),\n",
       " ('life', 16),\n",
       " ('less', 16),\n",
       " ('struck', 16),\n",
       " ('lit', 16),\n",
       " ('four', 15),\n",
       " ('dimensions', 15),\n",
       " ('each', 15),\n",
       " ('whole', 15),\n",
       " ('bright', 15),\n",
       " ('knew', 15),\n",
       " ('story', 15),\n",
       " ('running', 15),\n",
       " ('creatures', 15),\n",
       " ('palace', 15),\n",
       " ('find', 15),\n",
       " ('wood', 15),\n",
       " ('being', 14),\n",
       " ('large', 14),\n",
       " ('without', 14),\n",
       " ('right', 14),\n",
       " ('men', 14),\n",
       " ('odd', 14),\n",
       " ('good', 14),\n",
       " ('told', 14),\n",
       " ('next', 14),\n",
       " ('feel', 14),\n",
       " ('ever', 14),\n",
       " ('bushes', 14),\n",
       " ('coming', 14),\n",
       " ('age', 14),\n",
       " ('passed', 13),\n",
       " ('dinner', 13),\n",
       " ('lamp', 13),\n",
       " ('clear', 13),\n",
       " ('rose', 13),\n",
       " ('move', 13),\n",
       " ('tell', 13),\n",
       " ('small', 13),\n",
       " ('set', 13),\n",
       " ('understand', 13),\n",
       " ('silent', 13),\n",
       " ('times', 13),\n",
       " ('hundred', 13),\n",
       " ('doubt', 13),\n",
       " ('suppose', 13),\n",
       " ('dust', 13),\n",
       " ('nothing', 13),\n",
       " ('full', 13),\n",
       " ('every', 13),\n",
       " ('days', 13),\n",
       " ('dim', 13),\n",
       " ('open', 13),\n",
       " ('huge', 13),\n",
       " ('hall', 13),\n",
       " ('sea', 13),\n",
       " ('course', 12),\n",
       " ('clearly', 12),\n",
       " ('others', 12),\n",
       " ('am', 12),\n",
       " ('vanished', 12),\n",
       " ('since', 12),\n",
       " ('travelling', 12),\n",
       " ('remember', 12),\n",
       " ('gave', 12),\n",
       " ('sound', 12),\n",
       " ('happened', 12),\n",
       " ('trees', 12),\n",
       " ('creature', 12),\n",
       " ('vast', 12),\n",
       " ('beautiful', 12),\n",
       " ('pocket', 12),\n",
       " ('nature', 12),\n",
       " ('grey', 11),\n",
       " ('line', 11),\n",
       " ('really', 11),\n",
       " ('because', 11),\n",
       " ('simply', 11),\n",
       " ('figure', 11),\n",
       " ('eight', 11),\n",
       " ('done', 11),\n",
       " ('save', 11),\n",
       " ('possibly', 11),\n",
       " ('absolutely', 11),\n",
       " ('several', 11),\n",
       " ('conditions', 11),\n",
       " ('second', 11),\n",
       " ('sense', 11),\n",
       " ('queer', 11),\n",
       " ('evening', 11),\n",
       " ('hesitated', 11),\n",
       " ('glass', 11),\n",
       " ('sudden', 11),\n",
       " ('stars', 11),\n",
       " ('growing', 11),\n",
       " ('pedestal', 11),\n",
       " ('appeared', 11),\n",
       " ('thick', 11),\n",
       " ('altogether', 11),\n",
       " ('further', 11),\n",
       " ('judged', 11),\n",
       " ('blackness', 11),\n",
       " ('upper', 11),\n",
       " ('camphor', 11),\n",
       " ('slower', 11),\n",
       " ('pale', 10),\n",
       " ('caught', 10),\n",
       " ('instance', 10),\n",
       " ('wrong', 10),\n",
       " ('idea', 10),\n",
       " ('always', 10),\n",
       " ('quite', 10),\n",
       " ('work', 10),\n",
       " ('exactly', 10),\n",
       " ('animal', 10),\n",
       " ('better', 10),\n",
       " ('let', 10),\n",
       " ('walked', 10),\n",
       " ('larger', 10),\n",
       " ('m', 10),\n",
       " ('believe', 10),\n",
       " ('travelled', 10),\n",
       " ('bars', 10),\n",
       " ('watch', 10),\n",
       " ('either', 10),\n",
       " ('stopped', 10),\n",
       " ('sleep', 10),\n",
       " ('peculiar', 10),\n",
       " ('house', 10),\n",
       " ('humanity', 10),\n",
       " ('lawn', 10),\n",
       " ('creeping', 10),\n",
       " ('imagine', 10),\n",
       " ('memory', 10),\n",
       " ('broken', 10),\n",
       " ('strong', 10),\n",
       " ('determined', 10),\n",
       " ('ruins', 10),\n",
       " ('few', 10),\n",
       " ('comfort', 10),\n",
       " ('porcelain', 10),\n",
       " ('arms', 10),\n",
       " ('iron', 10),\n",
       " ('wells', 9),\n",
       " ('real', 9),\n",
       " ('nor', 9),\n",
       " ('explain', 9),\n",
       " ('ago', 9),\n",
       " ('moving', 9),\n",
       " ('travel', 9),\n",
       " ('interest', 9),\n",
       " ('arm', 9),\n",
       " ('shoulder', 9),\n",
       " ('bar', 9),\n",
       " ('laughed', 9),\n",
       " ('stared', 9),\n",
       " ('moved', 9),\n",
       " ('below', 9),\n",
       " ('point', 9),\n",
       " ('none', 9),\n",
       " ('met', 9),\n",
       " ('standing', 9),\n",
       " ('journalist', 9),\n",
       " ('opened', 9),\n",
       " ('brown', 9),\n",
       " ('faint', 9),\n",
       " ('question', 9),\n",
       " ('rest', 9),\n",
       " ('apparently', 9),\n",
       " ('blue', 9),\n",
       " ('buildings', 9),\n",
       " ('hung', 9),\n",
       " ('stone', 9),\n",
       " ('lost', 9),\n",
       " ('fancied', 9),\n",
       " ('entered', 9),\n",
       " ('near', 9),\n",
       " ('necessity', 9),\n",
       " ('alone', 9),\n",
       " ('social', 9),\n",
       " ('perfect', 9),\n",
       " ('doors', 9),\n",
       " ('shone', 8),\n",
       " ('follow', 8),\n",
       " ('length', 8),\n",
       " ('became', 8),\n",
       " ('fact', 8),\n",
       " ('young', 8),\n",
       " ('dimension', 8),\n",
       " ('surface', 8),\n",
       " ('telling', 8),\n",
       " ('forward', 8),\n",
       " ('your', 8),\n",
       " ('model', 8),\n",
       " ('together', 8),\n",
       " ('motion', 8),\n",
       " ('faster', 8),\n",
       " ('itself', 8),\n",
       " ('beside', 8),\n",
       " ('take', 8),\n",
       " ('eye', 8),\n",
       " ('wanted', 8),\n",
       " ('cut', 8),\n",
       " ('faces', 8),\n",
       " ('meat', 8),\n",
       " ('thinking', 8),\n",
       " ('noticed', 8),\n",
       " ('assured', 8),\n",
       " ('change', 8),\n",
       " ('horrible', 8),\n",
       " ('swiftly', 8),\n",
       " ('unknown', 8),\n",
       " ('everything', 8),\n",
       " ('drove', 8),\n",
       " ('instead', 8),\n",
       " ('race', 8),\n",
       " ('remote', 8),\n",
       " ('figures', 8),\n",
       " ('pretty', 8),\n",
       " ('thousand', 8),\n",
       " ('children', 8),\n",
       " ('building', 8),\n",
       " ('floor', 8),\n",
       " ('many', 8),\n",
       " ('least', 8),\n",
       " ('view', 8),\n",
       " ('abundant', 8),\n",
       " ('security', 8),\n",
       " ('strength', 8),\n",
       " ('physical', 8),\n",
       " ('needs', 8),\n",
       " ('triumph', 8),\n",
       " ('decay', 8),\n",
       " ('cold', 8),\n",
       " ('sleeping', 8),\n",
       " ('daylight', 8),\n",
       " ('within', 8),\n",
       " ('taken', 8),\n",
       " ('hastily', 8),\n",
       " ('glare', 8),\n",
       " ('shaft', 8),\n",
       " ('eloi', 8),\n",
       " ('south', 8),\n",
       " ('box', 8),\n",
       " ('forest', 8),\n",
       " ('shall', 7),\n",
       " ('mean', 7),\n",
       " ('need', 7),\n",
       " ('natural', 7),\n",
       " ('except', 7),\n",
       " ('trace', 7),\n",
       " ('high', 7),\n",
       " ('certainly', 7),\n",
       " ('hard', 7),\n",
       " ('different', 7),\n",
       " ('sure', 7),\n",
       " ('present', 7),\n",
       " ('become', 7),\n",
       " ('stop', 7),\n",
       " ('account', 7),\n",
       " ('attention', 7),\n",
       " ('case', 7),\n",
       " ('leave', 7),\n",
       " ('slowly', 7),\n",
       " ('held', 7),\n",
       " ('mechanism', 7),\n",
       " ('want', 7),\n",
       " ('saddle', 7),\n",
       " ('nearly', 7),\n",
       " ('plain', 7),\n",
       " ('spoke', 7),\n",
       " ('laughing', 7),\n",
       " ('sounds', 7),\n",
       " ('shadows', 7),\n",
       " ('puzzled', 7),\n",
       " ('somehow', 7),\n",
       " ('comes', 7),\n",
       " ('mouth', 7),\n",
       " ('colour', 7),\n",
       " ('intense', 7),\n",
       " ('pushed', 7),\n",
       " ('seem', 7),\n",
       " ('rare', 7),\n",
       " ('kept', 7),\n",
       " ('rising', 7),\n",
       " ('worn', 7),\n",
       " ('shadow', 7),\n",
       " ('short', 7),\n",
       " ('ears', 7),\n",
       " ('fast', 7),\n",
       " ('splendid', 7),\n",
       " ('shivered', 7),\n",
       " ('longer', 7),\n",
       " ('absolute', 7),\n",
       " ('beyond', 7),\n",
       " ('tree', 7),\n",
       " ('touched', 7),\n",
       " ('breathing', 7),\n",
       " ('violently', 7),\n",
       " ('fancy', 7),\n",
       " ('levers', 7),\n",
       " ('neck', 7),\n",
       " ('intellectual', 7),\n",
       " ('loose', 7),\n",
       " ('number', 7),\n",
       " ('foot', 7),\n",
       " ('windows', 7),\n",
       " ('metal', 7),\n",
       " ('seated', 7),\n",
       " ('fruit', 7),\n",
       " ('tired', 7),\n",
       " ('river', 7),\n",
       " ('valley', 7),\n",
       " ('living', 7),\n",
       " ('sunset', 7),\n",
       " ('during', 7),\n",
       " ('intelligence', 7),\n",
       " ('grow', 7),\n",
       " ('pleasant', 7),\n",
       " ('trying', 7),\n",
       " ('narrow', 7),\n",
       " ('water', 7),\n",
       " ('beach', 7),\n",
       " ('returned', 7),\n",
       " ('fallen', 7),\n",
       " ('underground', 7),\n",
       " ('thousands', 7),\n",
       " ('motionless', 7),\n",
       " ('dream', 7),\n",
       " ('speak', 6),\n",
       " ('matter', 6),\n",
       " ('burned', 6),\n",
       " ('silver', 6),\n",
       " ('carefully', 6),\n",
       " ('hair', 6),\n",
       " ('existence', 6),\n",
       " ('body', 6),\n",
       " ('wait', 6),\n",
       " ('fourth', 6),\n",
       " ('direction', 6),\n",
       " ('making', 6),\n",
       " ('words', 6),\n",
       " ('evidently', 6),\n",
       " ('getting', 6),\n",
       " ('means', 6),\n",
       " ('hope', 6),\n",
       " ('reason', 6),\n",
       " ('vague', 6),\n",
       " ('suggested', 6),\n",
       " ('cried', 6),\n",
       " ('trick', 6),\n",
       " ('ivory', 6),\n",
       " ('front', 6),\n",
       " ('drew', 6),\n",
       " ('chair', 6),\n",
       " ('incredible', 6),\n",
       " ('pointed', 6),\n",
       " ('forth', 6),\n",
       " ('wind', 6),\n",
       " ('bare', 6),\n",
       " ('impression', 6),\n",
       " ('flickering', 6),\n",
       " ('dance', 6),\n",
       " ('showed', 6),\n",
       " ('confusion', 6),\n",
       " ('late', 6),\n",
       " ('paper', 6),\n",
       " ('seven', 6),\n",
       " ('quiet', 6),\n",
       " ('hear', 6),\n",
       " ('warm', 6),\n",
       " ('blood', 6),\n",
       " ('waiting', 6),\n",
       " ('hot', 6),\n",
       " ('home', 6),\n",
       " ('started', 6),\n",
       " ('reached', 6),\n",
       " ('smoking', 6),\n",
       " ('lived', 6),\n",
       " ('thud', 6),\n",
       " ('garden', 6),\n",
       " ('afraid', 6),\n",
       " ('convey', 6),\n",
       " ('unpleasant', 6),\n",
       " ('fall', 6),\n",
       " ('twilight', 6),\n",
       " ('possible', 6),\n",
       " ('hail', 6),\n",
       " ('turf', 6),\n",
       " ('smoke', 6),\n",
       " ('nearer', 6),\n",
       " ('straight', 6),\n",
       " ('reminded', 6),\n",
       " ('sight', 6),\n",
       " ('confidence', 6),\n",
       " ('danger', 6),\n",
       " ('hitherto', 6),\n",
       " ('import', 6),\n",
       " ('corner', 6),\n",
       " ('watching', 6),\n",
       " ('slow', 6),\n",
       " ('distance', 6),\n",
       " ('slope', 6),\n",
       " ('plants', 6),\n",
       " ('disappeared', 6),\n",
       " ('close', 6),\n",
       " ('signs', 6),\n",
       " ('truth', 6),\n",
       " ('animals', 6),\n",
       " ('fate', 6),\n",
       " ('problem', 6),\n",
       " ('sometimes', 6),\n",
       " ('slept', 6),\n",
       " ('panels', 6),\n",
       " ('use', 6),\n",
       " ('stir', 6),\n",
       " ('mystery', 6),\n",
       " ('presence', 6),\n",
       " ('machinery', 6),\n",
       " ('theory', 6),\n",
       " ('heat', 6),\n",
       " ('species', 6),\n",
       " ('familiar', 6),\n",
       " ('convenient', 5),\n",
       " ('paradox', 5),\n",
       " ('accepted', 5),\n",
       " ('anything', 5),\n",
       " ('thickness', 5),\n",
       " ('object', 5),\n",
       " ('tendency', 5),\n",
       " ('beginning', 5),\n",
       " ('slight', 5),\n",
       " ('difference', 5),\n",
       " ('foolish', 5),\n",
       " ('hold', 5),\n",
       " ('state', 5),\n",
       " ('lips', 5),\n",
       " ('pause', 5),\n",
       " ('gently', 5),\n",
       " ('recognized', 5),\n",
       " ('smiled', 5),\n",
       " ('velocity', 5),\n",
       " ('himself', 5),\n",
       " ('wild', 5),\n",
       " ('deep', 5),\n",
       " ('wonder', 5),\n",
       " ('metallic', 5),\n",
       " ('clock', 5),\n",
       " ('explanation', 5),\n",
       " ('tables', 5),\n",
       " ('also', 5),\n",
       " ('brass', 5),\n",
       " ('low', 5),\n",
       " ('watched', 5),\n",
       " ('pressed', 5),\n",
       " ('pass', 5),\n",
       " ('waste', 5),\n",
       " ('changed', 5),\n",
       " ('breath', 5),\n",
       " ('flame', 5),\n",
       " ('ghost', 5),\n",
       " ('pipe', 5),\n",
       " ('interval', 5),\n",
       " ('thursday', 5),\n",
       " ('simple', 5),\n",
       " ('common', 5),\n",
       " ('led', 5),\n",
       " ('corridor', 5),\n",
       " ('complete', 5),\n",
       " ('perfectly', 5),\n",
       " ('holding', 5),\n",
       " ('perceived', 5),\n",
       " ('ll', 5),\n",
       " ('spirit', 5),\n",
       " ('surprise', 5),\n",
       " ('coat', 5),\n",
       " ('doorway', 5),\n",
       " ('word', 5),\n",
       " ('brighter', 5),\n",
       " ('till', 5),\n",
       " ('both', 5),\n",
       " ('remained', 5),\n",
       " ('startled', 5),\n",
       " ('true', 5),\n",
       " ('ceased', 5),\n",
       " ('ten', 5),\n",
       " ('falling', 5),\n",
       " ('hazy', 5),\n",
       " ('sensations', 5),\n",
       " ('helpless', 5),\n",
       " ('suggestion', 5),\n",
       " ('early', 5),\n",
       " ('fair', 5),\n",
       " ('civilization', 5),\n",
       " ('occurred', 5),\n",
       " ('resolved', 5),\n",
       " ('incontinently', 5),\n",
       " ('shape', 5),\n",
       " ('carried', 5),\n",
       " ('hour', 5),\n",
       " ('inhuman', 5),\n",
       " ('distinct', 5),\n",
       " ('wall', 5),\n",
       " ('clad', 5),\n",
       " ('rich', 5),\n",
       " ('voices', 5),\n",
       " ('heads', 5),\n",
       " ('beauty', 5),\n",
       " ('sweet', 5),\n",
       " ('ease', 5),\n",
       " ('flinging', 5),\n",
       " ('forgotten', 5),\n",
       " ('staggered', 5),\n",
       " ('knowledge', 5),\n",
       " ('naturally', 5),\n",
       " ('general', 5),\n",
       " ('deserted', 5),\n",
       " ('mass', 5),\n",
       " ('heaps', 5),\n",
       " ('fruits', 5),\n",
       " ('lower', 5),\n",
       " ('nevertheless', 5),\n",
       " ('spite', 5),\n",
       " ('cattle', 5),\n",
       " ('later', 5),\n",
       " ('language', 5),\n",
       " ('discovered', 5),\n",
       " ('cries', 5),\n",
       " ('calm', 5),\n",
       " ('crest', 5),\n",
       " ('planet', 5),\n",
       " ('masses', 5),\n",
       " ('experience', 5),\n",
       " ('form', 5),\n",
       " ('yellow', 5),\n",
       " ('horizon', 5),\n",
       " ('west', 5),\n",
       " ('steadily', 5),\n",
       " ('hither', 5),\n",
       " ('thither', 5),\n",
       " ('golden', 5),\n",
       " ('restless', 5),\n",
       " ('energy', 5),\n",
       " ('weak', 5),\n",
       " ('pain', 5),\n",
       " ('north', 5),\n",
       " ('covered', 5),\n",
       " ('whose', 5),\n",
       " ('circumstances', 5),\n",
       " ('probably', 5),\n",
       " ('horror', 5),\n",
       " ('land', 5),\n",
       " ('path', 5),\n",
       " ('reflection', 5),\n",
       " ('safe', 5),\n",
       " ('afternoon', 5),\n",
       " ('best', 5),\n",
       " ('dreaded', 5),\n",
       " ('nights', 5),\n",
       " ('clambering', 5),\n",
       " ('fingers', 5),\n",
       " ('edge', 5),\n",
       " ('habit', 5),\n",
       " ('museum', 5),\n",
       " ('burning', 5),\n",
       " ('killing', 5),\n",
       " ('eastward', 5),\n",
       " ('grass', 5),\n",
       " ('brightly', 4),\n",
       " ('flashed', 4),\n",
       " ('free', 4),\n",
       " ('geometry', 4),\n",
       " ('expect', 4),\n",
       " ('begin', 4),\n",
       " ('admit', 4),\n",
       " ('having', 4),\n",
       " ('does', 4),\n",
       " ('proceeded', 4),\n",
       " ('call', 4),\n",
       " ('latter', 4),\n",
       " ('cigar', 4),\n",
       " ('provincial', 4),\n",
       " ('mayor', 4),\n",
       " ('particularly', 4),\n",
       " ('manner', 4),\n",
       " ('curious', 4),\n",
       " ('proper', 4),\n",
       " ('weather', 4),\n",
       " ('movement', 4),\n",
       " ('freedom', 4),\n",
       " ('passing', 4),\n",
       " ('miles', 4),\n",
       " ('difficulty', 4),\n",
       " ('savage', 4),\n",
       " ('show', 4),\n",
       " ('laughter', 4),\n",
       " ('weary', 4),\n",
       " ('smiling', 4),\n",
       " ('passage', 4),\n",
       " ('scarcely', 4),\n",
       " ('substance', 4),\n",
       " ('legs', 4),\n",
       " ('singularly', 4),\n",
       " ('seat', 4),\n",
       " ('turning', 4),\n",
       " ('blown', 4),\n",
       " ('indistinct', 4),\n",
       " ('damned', 4),\n",
       " ('journey', 4),\n",
       " ('visible', 4),\n",
       " ('serious', 4),\n",
       " ('remarked', 4),\n",
       " ('asked', 4),\n",
       " ('plausible', 4),\n",
       " ('morrow', 4),\n",
       " ('broad', 4),\n",
       " ('parts', 4),\n",
       " ('bench', 4),\n",
       " ('sheets', 4),\n",
       " ('touch', 4),\n",
       " ('frame', 4),\n",
       " ('five', 4),\n",
       " ('ve', 4),\n",
       " ('besides', 4),\n",
       " ('previous', 4),\n",
       " ('absence', 4),\n",
       " ('week', 4),\n",
       " ('facing', 4),\n",
       " ('smile', 4),\n",
       " ('remembered', 4),\n",
       " ('brought', 4),\n",
       " ('doing', 4),\n",
       " ('recover', 4),\n",
       " ('resumed', 4),\n",
       " ('curiosity', 4),\n",
       " ('friend', 4),\n",
       " ('business', 4),\n",
       " ('clothes', 4),\n",
       " ('easy', 4),\n",
       " ('salt', 4),\n",
       " ('give', 4),\n",
       " ('displayed', 4),\n",
       " ('lying', 4),\n",
       " ('afterwards', 4),\n",
       " ('voice', 4),\n",
       " ('rail', 4),\n",
       " ('putting', 4),\n",
       " ('machines', 4),\n",
       " ('skull', 4),\n",
       " ('starting', 4),\n",
       " ('stopping', 4),\n",
       " ('sensation', 4),\n",
       " ('teeth', 4),\n",
       " ('extreme', 4),\n",
       " ('position', 4),\n",
       " ('eddying', 4),\n",
       " ('murmur', 4),\n",
       " ('headlong', 4),\n",
       " ('pace', 4),\n",
       " ('painful', 4),\n",
       " ('glimpse', 4),\n",
       " ('circling', 4),\n",
       " ('spread', 4),\n",
       " ('rise', 4),\n",
       " ('dials', 4),\n",
       " ('year', 4),\n",
       " ('flung', 4),\n",
       " ('scarce', 4),\n",
       " ('dread', 4),\n",
       " ('possession', 4),\n",
       " ('built', 4),\n",
       " ('risk', 4),\n",
       " ('profound', 4),\n",
       " ('blow', 4),\n",
       " ('inevitable', 4),\n",
       " ('purple', 4),\n",
       " ('dancing', 4),\n",
       " ('innumerable', 4),\n",
       " ('colossal', 4),\n",
       " ('sides', 4),\n",
       " ('greatly', 4),\n",
       " ('passion', 4),\n",
       " ('foul', 4),\n",
       " ('tall', 4),\n",
       " ('shining', 4),\n",
       " ('frenzy', 4),\n",
       " ('emerged', 4),\n",
       " ('frail', 4),\n",
       " ('oddly', 4),\n",
       " ('harsh', 4),\n",
       " ('features', 4),\n",
       " ('effort', 4),\n",
       " ('gesture', 4),\n",
       " ('abruptly', 4),\n",
       " ('art', 4),\n",
       " ('fro', 4),\n",
       " ('delicate', 4),\n",
       " ('crowd', 4),\n",
       " ('grotesque', 4),\n",
       " ('ways', 4),\n",
       " ('places', 4),\n",
       " ('learn', 4),\n",
       " ('mine', 4),\n",
       " ('meaning', 4),\n",
       " ('name', 4),\n",
       " ('speedily', 4),\n",
       " ('setting', 4),\n",
       " ('thames', 4),\n",
       " ('mile', 4),\n",
       " ('help', 4),\n",
       " ('ruinous', 4),\n",
       " ('heap', 4),\n",
       " ('walls', 4),\n",
       " ('realized', 4),\n",
       " ('houses', 4),\n",
       " ('force', 4),\n",
       " ('population', 4),\n",
       " ('balanced', 4),\n",
       " ('rarely', 4),\n",
       " ('secure', 4),\n",
       " ('palaces', 4),\n",
       " ('mankind', 4),\n",
       " ('leaving', 4),\n",
       " ('fight', 4),\n",
       " ('balance', 4),\n",
       " ('gradually', 4),\n",
       " ('current', 4),\n",
       " ('attained', 4),\n",
       " ('struggle', 4),\n",
       " ('increasing', 4),\n",
       " ('sunlight', 4),\n",
       " ('east', 4),\n",
       " ('folly', 4),\n",
       " ('reach', 4),\n",
       " ('empty', 4),\n",
       " ('slipped', 4),\n",
       " ('child', 4),\n",
       " ('blundering', 4),\n",
       " ('terror', 4),\n",
       " ('crept', 4),\n",
       " ('loss', 4),\n",
       " ('groping', 4),\n",
       " ('fairly', 4),\n",
       " ('failed', 4),\n",
       " ...]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab.token_freqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f74a784c-7e27-4b75-b7f5-917151e86f77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2261, 1267, 1245, 1155, 816, 695, 552, 541, 443, 440]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出所有的词频\n",
    "freqs = [freq for token, freq in vocab.token_freqs]\n",
    "freqs[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4ad451ac-70de-439c-8c6a-05b9a16f347b",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'torch'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[24], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01md2l\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m torch \u001b[38;5;28;01mas\u001b[39;00m d2l\n",
      "File \u001b[1;32md:\\developer\\python396\\lib\\site-packages\\d2l\\torch.py:29\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[38;5;66;03m# Defined in file: ./chapter_preface/index.md\u001b[39;00m\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m---> 29\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m     30\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorchvision\u001b[39;00m\n\u001b[0;32m     31\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPIL\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'torch'"
     ]
    }
   ],
   "source": [
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "205d528e-76e1-4d0d-b588-f3f8b264c2f5",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'd2l' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43md2l\u001b[49m\u001b[38;5;241m.\u001b[39mplot(freqs, xlabel\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtoken:x\u001b[39m\u001b[38;5;124m'\u001b[39m, ylabel\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfrequency:n(x)\u001b[39m\u001b[38;5;124m'\u001b[39m, xscale\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlog\u001b[39m\u001b[38;5;124m'\u001b[39m, yscale\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlog\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'd2l' is not defined"
     ]
    }
   ],
   "source": [
    "d2l.plot(freqs, xlabel='token:x', ylabel='frequency:n(x)', xscale='log', yscale='log')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8e27c59-4dea-4a40-a205-1e8645c82259",
   "metadata": {},
   "source": [
    "#### 二元语法bi-grams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2b0bfbbe-41d0-4157-8f75-c9e49366c7f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('the', 'time'),\n",
       " ('time', 'machine'),\n",
       " ('machine', 'by'),\n",
       " ('by', 'h'),\n",
       " ('h', 'g'),\n",
       " ('g', 'wells'),\n",
       " ('wells', 'i'),\n",
       " ('i', 'the'),\n",
       " ('the', 'time'),\n",
       " ('time', 'traveller')]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gram_tokens = [pair for pair in zip(corpus[:-1], corpus[1:])]\n",
    "gram_tokens[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "38b1d64d-36a9-4682-923e-3fb60dd52d4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('of', 'the'), 309),\n",
       " (('in', 'the'), 169),\n",
       " (('i', 'had'), 130),\n",
       " (('i', 'was'), 112),\n",
       " (('and', 'the'), 109),\n",
       " (('the', 'time'), 102),\n",
       " (('it', 'was'), 99),\n",
       " (('to', 'the'), 85),\n",
       " (('as', 'i'), 78),\n",
       " (('of', 'a'), 73)]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigram_vocab = Vocab(gram_tokens)\n",
    "bigram_vocab.token_freqs[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff09f363-b823-4c82-b12d-b7698159f98d",
   "metadata": {},
   "source": [
    "#### 三元法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e89af6f1-346c-4967-8dde-64e1dcfe0313",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('the', 'time', 'machine'),\n",
       " ('time', 'machine', 'by'),\n",
       " ('machine', 'by', 'h'),\n",
       " ('by', 'h', 'g'),\n",
       " ('h', 'g', 'wells'),\n",
       " ('g', 'wells', 'i'),\n",
       " ('wells', 'i', 'the'),\n",
       " ('i', 'the', 'time'),\n",
       " ('the', 'time', 'traveller'),\n",
       " ('time', 'traveller', 'for')]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trigra_tokens = [triple for triple in zip(corpus[:-2], corpus[1:-1], corpus[2:])]\n",
    "trigra_tokens[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91a3b340-6c23-4df5-9365-1dfc0e8fca40",
   "metadata": {},
   "source": [
    "#### 对比一元, 二元, 三元语法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "01944da5-7958-43d4-bce7-5fa491ccdb3b",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'trigram_vocab' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[29], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m bigram_freqs \u001b[38;5;241m=\u001b[39m [freq \u001b[38;5;28;01mfor\u001b[39;00m token, freq \u001b[38;5;129;01min\u001b[39;00m bigram_vocab\u001b[38;5;241m.\u001b[39mtoken_freqs]\n\u001b[1;32m----> 2\u001b[0m trigram_freqs \u001b[38;5;241m=\u001b[39m [freq \u001b[38;5;28;01mfor\u001b[39;00m token, freq \u001b[38;5;129;01min\u001b[39;00m \u001b[43mtrigram_vocab\u001b[49m\u001b[38;5;241m.\u001b[39mtoken_freqs]\n",
      "\u001b[1;31mNameError\u001b[0m: name 'trigram_vocab' is not defined"
     ]
    }
   ],
   "source": [
    "bigram_freqs = [freq for token, freq in bigram_vocab.token_freqs]\n",
    "trigram_freqs = [freq for token, freq in trigram_vocab.token_freqs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5ab3a508-6ee8-4b26-b9e1-bda5a0b64420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('the', 'time', 'traveller'), 59),\n",
       " (('the', 'time', 'machine'), 30),\n",
       " (('the', 'medical', 'man'), 24),\n",
       " (('it', 'seemed', 'to'), 16),\n",
       " (('it', 'was', 'a'), 15),\n",
       " (('here', 'and', 'there'), 15),\n",
       " (('seemed', 'to', 'me'), 14),\n",
       " (('i', 'did', 'not'), 14),\n",
       " (('i', 'saw', 'the'), 13),\n",
       " (('i', 'began', 'to'), 13)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trigram_vocab = Vocab(trigra_tokens)\n",
    "trigram_vocab.token_freqs[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2af0444b-7216-47d4-8235-14e799c798cf",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'd2l' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[31], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43md2l\u001b[49m\u001b[38;5;241m.\u001b[39mplot([freqs, bigram_freqs, trigram_freqs], xlabel\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtoken:x\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[0;32m      2\u001b[0m         ylabel\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfrequency:n(x)\u001b[39m\u001b[38;5;124m'\u001b[39m, xscale\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlog\u001b[39m\u001b[38;5;124m'\u001b[39m, yscale\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlog\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      3\u001b[0m         legend\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munigram\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbigram\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrigram\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "\u001b[1;31mNameError\u001b[0m: name 'd2l' is not defined"
     ]
    }
   ],
   "source": [
    "d2l.plot([freqs, bigram_freqs, trigram_freqs], xlabel='token:x', \n",
    "        ylabel='frequency:n(x)', xscale='log', yscale='log',\n",
    "        legend=['unigram', 'bigram', 'trigram'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f211b26-d2a3-4e55-abb1-ec1defca1dbb",
   "metadata": {},
   "source": [
    "### 序列数据采样"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "805adaeb-7d22-44d7-bbfa-e29ca0912f35",
   "metadata": {},
   "source": [
    "#### 随机采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d714ddea-3439-4edf-b9aa-8a93735b3f02",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2ce4237c-14a6-471a-8e6a-d80c515a578c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus = np.arange(35)\n",
    "num_steps = 5 #每个数据子序列的个数(步长)\n",
    "num_subseqs = (len(corpus) - 1) // num_steps  #计算数据子序列的个数\n",
    "num_subseqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "837e7fac-46bd-4cb3-9bba-7c374d52a9e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 5, 10, 15, 20, 25]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算每个子序列的起始索引\n",
    "initial_indices = list(range(0, num_subseqs*num_steps, num_steps))\n",
    "initial_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4184d9ce-f9b7-4907-bab7-45847cdb523b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[15, 20, 5, 25, 0, 10]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将起始索引随机更换顺序\n",
    "random.shuffle(initial_indices)\n",
    "initial_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b30205d2-3e34-4d50-b166-f3f43f06650f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3422cfcf-6cbf-4538-8637-165d1514e246",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 随机采样\n",
    "def seq_data_iter_random(corpus, batch_size, num_steps):\n",
    "    #随机偏移量\n",
    "    corpus = corpus[random.randint(0, num_steps - 1) : ]\n",
    "    num_subseqs = (len(corpus) - 1) // num_steps\n",
    "    initial_indices = list(range(0, num_subseqs*num_steps, num_steps))\n",
    "    #将起始索引随机更换顺序(为了随机效果)\n",
    "    random.shuffle(initial_indices)\n",
    "     #计算分批数量\n",
    "    num_batches = num_subseqs // batch_size\n",
    "    for i in range(0, batch_size * num_batches, batch_size):\n",
    "        initial_indices_per_batch = initial_indices[i : i + batch_size]\n",
    "        #取数据\n",
    "        X = [data(j) for j in initial_indices_per_batch]\n",
    "        Y = [data(j+1) for j in initial_indices_per_batch]\n",
    "        yield torch.tensor(X), torch.tensor(Y)\n",
    "         \n",
    "def data(pos):\n",
    "        return corpus[pos: pos + num_steps]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ed7351af-646e-49da-8a8c-0350d1b53425",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X: tensor([[10, 11, 12, 13, 14],\n",
      "        [20, 21, 22, 23, 24]], dtype=torch.int32) \n",
      "Y: tensor([[11, 12, 13, 14, 15],\n",
      "        [21, 22, 23, 24, 25]], dtype=torch.int32)\n",
      "X: tensor([[ 5,  6,  7,  8,  9],\n",
      "        [25, 26, 27, 28, 29]], dtype=torch.int32) \n",
      "Y: tensor([[ 6,  7,  8,  9, 10],\n",
      "        [26, 27, 28, 29, 30]], dtype=torch.int32)\n",
      "X: tensor([[ 0,  1,  2,  3,  4],\n",
      "        [15, 16, 17, 18, 19]], dtype=torch.int32) \n",
      "Y: tensor([[ 1,  2,  3,  4,  5],\n",
      "        [16, 17, 18, 19, 20]], dtype=torch.int32)\n"
     ]
    }
   ],
   "source": [
    "my_seq = list(range(35))\n",
    "for X, Y in seq_data_iter_random(my_seq, batch_size=2, num_steps=5):\n",
    "    print('X:', X, '\\nY:', Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e7317ffc-c1c6-4c7a-9148-fe7ffb8fc0a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
       " 1,\n",
       " 2,\n",
       " 3,\n",
       " 4,\n",
       " 5,\n",
       " 6,\n",
       " 7,\n",
       " 8,\n",
       " 9,\n",
       " 10,\n",
       " 11,\n",
       " 12,\n",
       " 13,\n",
       " 14,\n",
       " 15,\n",
       " 16,\n",
       " 17,\n",
       " 18,\n",
       " 19,\n",
       " 20,\n",
       " 21,\n",
       " 22,\n",
       " 23,\n",
       " 24,\n",
       " 25,\n",
       " 26,\n",
       " 27,\n",
       " 28,\n",
       " 29,\n",
       " 30,\n",
       " 31,\n",
       " 32,\n",
       " 33,\n",
       " 34]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "08b86cbf-b5b0-45ab-9576-43d840edab26",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=2\n",
    "num_tokens = ((len(my_seq) - 1) // batch_size) * batch_size\n",
    "num_tokens\n",
    "Xs = torch.tensor(my_seq[: num_tokens])\n",
    "Xs\n",
    "Xs = Xs.reshape(batch_size, -1)     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a875c26d-7647-4430-bbff-df1f7856eedd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 17])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "30063b2e-a2fd-41df-b3e9-330d7e566cbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16],\n",
       "        [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f279a018-edc0-4c3e-ad0c-3e90756a7580",
   "metadata": {},
   "source": [
    "#### 顺序采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "03d8bc10-3e97-4de4-b476-e20940415cbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def seq_data_iter_sequential(corpus, batch_size, num_steps):\n",
    "    # 加一个随机偏移量\n",
    "    offset = random.randint(0, num_steps)\n",
    "    # 有效token的长度\n",
    "    num_tokens = ((len(corpus) - offset - 1) // batch_size) * batch_size\n",
    "    Xs = torch.tensor(corpus[offset: offset + num_tokens])\n",
    "    Ys = torch.tensor(corpus[offset + 1: offset + num_tokens + 1])\n",
    "    Xs, Ys = Xs.reshape(batch_size, -1), Ys.reshape(batch_size, -1)\n",
    "    num_batches = Xs.shape[1] // num_steps\n",
    "    for i in range(0, num_steps * num_batches, num_steps):\n",
    "        X = Xs[:, i: i + num_steps]\n",
    "        Y = Ys[:, i: i + num_steps]\n",
    "        yield X, Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a4d81fd5-a2ba-4fba-a718-323c0ce26c42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X: tensor([[ 3,  4,  5,  6,  7],\n",
      "        [18, 19, 20, 21, 22]]) \n",
      "Y: tensor([[ 4,  5,  6,  7,  8],\n",
      "        [19, 20, 21, 22, 23]])\n",
      "X: tensor([[ 8,  9, 10, 11, 12],\n",
      "        [23, 24, 25, 26, 27]]) \n",
      "Y: tensor([[ 9, 10, 11, 12, 13],\n",
      "        [24, 25, 26, 27, 28]])\n",
      "X: tensor([[13, 14, 15, 16, 17],\n",
      "        [28, 29, 30, 31, 32]]) \n",
      "Y: tensor([[14, 15, 16, 17, 18],\n",
      "        [29, 30, 31, 32, 33]])\n"
     ]
    }
   ],
   "source": [
    "my_seq = list(range(35))\n",
    "for X, Y in seq_data_iter_sequential(my_seq, batch_size=2, num_steps=5):\n",
    "    print('X:', X, '\\nY:', Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9f510bb1-c921-43bf-8a47-16351159da45",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把两个采样函数包装到类中, 方便后续使用. \n",
    "class SeqDataLoader:\n",
    "    def __init__(self, batch_size, num_steps, use_random_iter, max_tokens):\n",
    "        if use_random_iter:\n",
    "            self.data_iter_fn = seq_data_iter_random\n",
    "        else:\n",
    "            self.data_iter_fn = seq_data_iter_sequential\n",
    "        \n",
    "        self.corpus, self.vocab = load_corpus_time_machine(max_tokens)\n",
    "        self.batch_size, self.num_steps = batch_size, num_steps\n",
    "        \n",
    "    def __iter__(self):\n",
    "        return self.data_iter_fn(self.corpus, self.batch_size, self.num_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4dab05e3-6f8e-4fa4-9259-58c7155c3017",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 返回数据迭代器和词汇表\n",
    "def load_data_time_machine(batch_size, num_steps, user_random_iter=False, max_tokens=10000):\n",
    "    data_iter = SeqDataLoader(batch_size, num_steps, user_random_iter, max_tokens)\n",
    "    return data_iter, data_iter.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "01df772b-e21a-4d88-82e0-703d786861ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size, num_steps = 2, 10\n",
    "loader, vocab = load_corpus_time_machine(batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "16437a45-62af-469d-b21a-82992a5575d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "for i in loader:\n",
    "    print(i)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5e238330-16c8-4108-9f0c-8125cf3a1ee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'<unk>': 0,\n",
       " ' ': 1,\n",
       " 'e': 2,\n",
       " 't': 3,\n",
       " 'a': 4,\n",
       " 'i': 5,\n",
       " 'n': 6,\n",
       " 'o': 7,\n",
       " 's': 8,\n",
       " 'h': 9,\n",
       " 'r': 10,\n",
       " 'd': 11,\n",
       " 'l': 12,\n",
       " 'm': 13,\n",
       " 'u': 14,\n",
       " 'c': 15,\n",
       " 'f': 16,\n",
       " 'w': 17,\n",
       " 'g': 18,\n",
       " 'y': 19,\n",
       " 'p': 20,\n",
       " 'b': 21,\n",
       " 'v': 22,\n",
       " 'k': 23,\n",
       " 'x': 24,\n",
       " 'z': 25,\n",
       " 'j': 26,\n",
       " 'q': 27}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab.token_to_idx"
   ]
  },
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   "execution_count": null,
   "id": "be6e19ff-e6a4-48c1-88fc-2688e59ef089",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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